scholarly journals Impaired Topographic Organization in Patients With Idiopathic Blepharospasm

2022 ◽  
Vol 12 ◽  
Author(s):  
Yanbing Hou ◽  
Lingyu Zhang ◽  
Qianqian Wei ◽  
Ruwei Ou ◽  
Jing Yang ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Small World ◽  
Clustering Coefficient ◽  
Classification Model ◽  
Imaging Features ◽  
Lasso Regression ◽  
Network Efficiency ◽  
Sensorimotor Network ◽  
Idiopathic Blepharospasm ◽  
And Gender

Background: Idiopathic blepharospasm (BSP) is a common adult-onset focal dystonia. Neuroimaging technology can be used to visualize functional and microstructural changes of the whole brain.Method: We used resting-state functional MRI (rs-fMRI) and graph theoretical analysis to explore the functional connectome in patients with BSP. Altogether 20 patients with BSP and 20 age- and gender-matched healthy controls (HCs) were included in the study. Measures of network topology were calculated, such as small-world parameters (clustering coefficient [Cp], the shortest path length [Lp]), network efficiency parameters (global efficiency [Eglob], local efficiency [Eloc]), and the nodal parameter (nodal efficiency [Enod]). In addition, the least absolute shrinkage and selection operator (LASSO) regression was adopted to determine the most critical imaging features, and the classification model using critical imaging features was constructed.Results: Compared with HCs, the BSP group showed significantly decreased Eloc. Imaging features of nodal centrality (Enod) were entered into the LASSO method, and the classification model was constructed with nine imaging nodes. The area under the curve (AUC) was 0.995 (95% CI: 0.973–1.000), and the sensitivity and specificity were 95% and 100%, respectively. Specifically, four imaging nodes within the sensorimotor network (SMN), cerebellum, and default mode network (DMN) held the prominent information. Compared with HCs, the BSP group showed significantly increased Enod in the postcentral region within the SMN, decreased Enod in the precentral region within the SMN, increased Enod in the medial cerebellum, and increased Enod in the precuneus within the DMN.Conclusion: The network model in BSP showed reduced local connectivity. Baseline connectomic measures derived from rs-fMRI data may be capable of identifying patients with BSP, and regions from the SMN, cerebellum, and DMN may provide key insights into the underlying pathophysiology of BSP.

Survivability of public transit network based on network structure entropy

2015 ◽  
Vol 26 (09) ◽  
pp. 1550104 ◽  
Author(s):  
Bai-Bai Fu ◽  
Lin Zhang ◽  
Shu-Bin Li ◽  
Yun-Xuan Li
Keyword(s):  
Network Model ◽  
Network Structure ◽  
Public Transit ◽  
Small World ◽  
Clustering Coefficient ◽  
Statistical Characteristics ◽  
Network Efficiency ◽  
Transit Network ◽  
Scale Free ◽  
The Public

In this work, we have collected 195 bus routes and 1433 bus stations of Jinan city as sample date to build up the public transit geospatial network model by applying space L method, until May 2014. Then, by analyzing the topological properties of public transit geospatial network model, which include degree and degree distribution, average shortest path length, clustering coefficient and betweenness, we get the conclusion that public transit network is a typical complex network with scale-free and small-world characteristics. Furthermore, in order to analyze the survivability of public transit network, we define new network structure entropy based on betweenness importance, and prove its correctness by giving that the new network structure entropy has the same statistical characteristics with network efficiency. Finally, the "inflexion zone" is discovered, which can be taken as the momentous indicator to determine the public transit network failure.

scholarly journals Support Vector Machine Based Feature Extraction for Gender Recognition From Objects Using Lasso Classifier

2020 ◽  
Author(s):  
Damodara Krishna Kishore Galla ◽  
BabuReddy Mukamalla ◽  
Rama Prakasha Reddy Chegireddy
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Classification Model ◽  
Support Vector ◽  
Gender Classification ◽  
Accuracy Score ◽  
Lasso Regression ◽  
Vehicle Recognition ◽  
And Gender ◽  
Human Faces

Abstract The blind people has their difficulty to identify the object moving around them, therefore with a high accuracy score object detection and human face recognition system will helps them in identifying the things around them with ease. In this research article,a minimum distance trainer for feature selection by accessing SVM feature optimization process, then LASSO classifier applied to perform object recognition and gender classification. Database of 100 images (50 male and 50 female face images considered from 5 different databases) and 10 categories of vehicle types are used for gender and vehicle recognition and classification. Original face image database used for the gender classification. This approach was implemented with dual classification model [(1) Recognizing or classifying human faces from various objects and (2) Classifying gender through face recognition] is made possible with the help of combining modified SIFT feature in combination with ridge regression (RR), elastic net (EN), lasso regression(LR) and lasso regression with Gaussian Support Vector Machines (LRGS) based classificatioins. The final classification results accurate are as follows RR- 89.6%, EN- 93.5%, LR-93.2% and the proposed approach is LRGS with 98.4% accurate detection rate with rediction names.

scholarly journals Altered Small-World Functional Network Topology in Patients with Optic Neuritis: A Resting-State fMRI Study

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ke Song ◽  
Juan Li ◽  
Yuanqiang Zhu ◽  
Fang Ren ◽  
Lingcan Cao ◽  
...  
Keyword(s):  
Resting State ◽  
Path Length ◽  
Small World ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Control Group ◽  
Network Efficiency ◽  
Characteristic Path Length ◽  
Brain Functional Connectivity ◽  
The Brain

Aim. This study investigated changes in small-world topology and brain functional connectivity in patients with optic neuritis (ON) by resting-state functional magnetic resonance imaging (rs-fMRI) and based on graph theory. Methods. A total of 21 patients with ON (8 males and 13 females) and 21 matched healthy control subjects (8 males and 13 females) were enrolled and underwent rs-fMRI. Data were preprocessed and the brain was divided into 116 regions of interest. Small-world network parameters and area under the integral curve (AUC) were calculated from pairwise brain interval correlation coefficients. Differences in brain network parameter AUCs between the 2 groups were evaluated with the independent sample t -test, and changes in brain connection strength between ON patients and control subjects were assessed by network-based statistical analysis. Results. In the sparsity range from 0.08 to 0.48, both groups exhibited small-world attributes. Compared to the control group, global network efficiency, normalized clustering coefficient, and small-world value were higher whereas the clustering coefficient value was lower in ON patients. There were no differences in characteristic path length, local network efficiency, and normalized characteristic path length between groups. In addition, ON patients had lower brain functional connectivity strength among the rolandic operculum, medial superior frontal gyrus, insula, median cingulate and paracingulate gyri, amygdala, superior parietal gyrus, inferior parietal gyrus, supramarginal gyrus, angular gyrus, lenticular nucleus, pallidum, superior temporal gyrus, and cerebellum compared to the control group ( P < 0.05 ). Conclusion. Patients with ON show typical “small world” topology that differed from that detected in HC brain networks. The brain network in ON has a small-world attribute but shows reduced and abnormal connectivity compared to normal subjects and likely causes symptoms of cognitive impairment.

scholarly journals Altered small-world functional network topology in patients with optic neuritis: A resting- state fMRI study

2020 ◽  
Author(s):  
Ke Song ◽  
Juan Li ◽  
Yuanqiang Zhu ◽  
Fang Ren ◽  
Lingcan Cao ◽  
...  
Keyword(s):  
Resting State ◽  
Path Length ◽  
Small World ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Control Group ◽  
Network Efficiency ◽  
Characteristic Path Length ◽  
Brain Functional Connectivity ◽  
The Brain

AbstractPurposeThis study investigated changes in small-world topology and brain functional connectivity in patients with optic neuritis (ON) by resting-state functional magnetic resonance imaging (rs-fMRI) and based on graph theory.MethodsA total of 21 patients with ON (8 males and 13 females) and 21 matched healthy control subjects (8 males and 13 females) were enrolled at the First Affiliated Hospital of Nanchang University and underwent rs-fMRI. Data were preprocessed and the brain was divided into 116 regions of interest. Small-world network parameters and area under the integral curve (AUC) were calculated from pairwise brain interval correlation coefficients. Differences in brain network parameter AUCs between the 2 groups were evaluated with the independent sample t-test, and changes in brain connection strength between ON patients and control subjects were assessed by network-based statistical analysis.ResultsIn the sparsity range from 0.08 to 0.48, both groups exhibited small-world attributes.Compared to the control group, global network efficiency, normalized clustering coefficient, and small-world value were higher whereas the clustering coefficient value was lower in ON patients. There were no differences in characteristic path length, local network efficiency, and normalized characteristic path length between groups. In addition, ON patients had lower brain functional connectivity strength among the rolandic operculum, medial superior frontal gyrus, insula, median cingulate and paracingulate gyri, amygdala, superior parietal gyrus, inferior parietal gyrus, supramarginal gyrus, angular gyrus, lenticular nucleus, pallidum, superior temporal gyrus, cerebellum_Crus1_L, and left cerebellum_Crus6_L compared to the control group (P < 0.05).ConclusionThe brain network in ON has a small-world attributes but shows reduced and abnormal connectivity compared to normal subjects. These findings provide a further insight into the neural pathogenesis of ON and reveal specific fMRI findings that can serve as diagnostic and prognostic indices.

The Subaltern Speak- A Study On The Culture Of The Arunachalis With Special Reference To Mamang Dai’s The Legends Of Pensam

2019 ◽  
Vol 5 (4) ◽  
pp. 14-20
Author(s):  
Ms. Cheryl Antonette Dumenil ◽  
Dr. Cheryl Davis
Keyword(s):  
Special Reference ◽  
Small World ◽  
Natural World ◽  
Arunachal Pradesh ◽  
North East India ◽  
North East ◽  
Shared History ◽  
History Of ◽  
And Gender ◽  
Insight Into

North- East India is an under veiled region with an awe-inspiring landscape, different groups of ethnic people, their culture and heritage. Contemporary writers from this region aspire towards a vision outside the tapered ethnic channel, and they represent a shared history. In their writings, the cultural memory is showcased, and the intensity of feeling overflows the labour of technique and craft. Mamang Dai presents a rare glimpse into the ecology, culture, life of the tribal people and history of the land of the dawn-lit mountains, Arunachal Pradesh, through her novel The Legends of Pensam. The word ‘Pensam’ in the title means ‘in-between’,  but it may also be interpreted as ‘the hidden spaces of the heart’. This is a small world where anything can happen. Being adherents of the animistic faith, the tribes here believe in co-existence with the natural world along with the presence of spirits in their forests and rivers. This paper attempts to draw an insight into the culture and gender of the Arunachalis with special reference to The Legends of Pensam by Mamang Dai.

scholarly journals POS1439 A MULTI-PREDICTOR MODEL TO PREDICT RISK OF SCLERODERMA RENAL CRISIS IN SYSTEMIC SCLEROSIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1004.1-1004
Author(s):  
D. Xu ◽  
R. Mu
Keyword(s):  
Systemic Sclerosis ◽  
Prediction Model ◽  
Renal Involvement ◽  
Area Under The Curve ◽  
Individual Risk ◽  
Multivariable Logistic Regression Analysis ◽  
Scleroderma Renal Crisis ◽  
Lasso Regression ◽  
Patients At Risk ◽  
Renal Crisis

Background:Scleroderma renal crisis (SRC) is a life-threatening syndrome. The early identification of patients at risk is essential for timely treatment to improve the outcome[1].Objectives:We aimed to provide a personalized tool to predict risk of SRC in systemic sclerosis (SSc).Methods:We tried to set up a SRC prediction model based on the PKUPH-SSc cohort of 302 SSc patients. The least absolute shrinkage and selection operator (Lasso) regression was used to optimize disease features. Multivariable logistic regression analysis was applied to build a SRC prediction model incorporating the features of SSc selected in the Lasso regression. Then, a multi-predictor nomogram combining clinical characteristics was constructed and evaluated by discrimination and calibration.Results:A multi-predictor nomogram for evaluating the risk of SRC was successfully developed. In the nomogram, four easily available predictors were contained including disease duration <2 years, cardiac involvement, anemia and corticosteroid >15mg/d exposure. The nomogram displayed good discrimination with an area under the curve (AUC) of 0.843 (95% CI: 0.797-0.882) and good calibration.Conclusion:The multi-predictor nomogram for SRC could be reliably and conveniently used to predict the individual risk of SRC in SSc patients, and be a step towards more personalized medicine.References:[1]Woodworth TG, Suliman YA, Li W, Furst DE, Clements P (2016) Scleroderma renal crisis and renal involvement in systemic sclerosis. Nat Rev Nephrol 12 (11):678-91.Disclosure of Interests:None declared

scholarly journals Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma

2019 ◽  
Vol 45 (10) ◽  
pp. 3193-3201 ◽  
Author(s):  
Yajuan Li ◽  
Xialing Huang ◽  
Yuwei Xia ◽  
Liling Long
Keyword(s):  
Machine Learning ◽  
Differential Diagnosis ◽  
Cell Carcinoma ◽  
Area Under The Curve ◽  
Image Features ◽  
Renal Tumors ◽  
Support Vector ◽  
Svm Classifier ◽  
Renal Oncocytoma ◽  
Lasso Regression

Abstract Purpose To explore the value of CT-enhanced quantitative features combined with machine learning for differential diagnosis of renal chromophobe cell carcinoma (chRCC) and renal oncocytoma (RO). Methods Sixty-one cases of renal tumors (chRCC = 44; RO = 17) that were pathologically confirmed at our hospital between 2008 and 2018 were retrospectively analyzed. All patients had undergone preoperative enhanced CT scans including the corticomedullary (CMP), nephrographic (NP), and excretory phases (EP) of contrast enhancement. Volumes of interest (VOIs), including lesions on the images, were manually delineated using the RadCloud platform. A LASSO regression algorithm was used to screen the image features extracted from all VOIs. Five machine learning classifications were trained to distinguish chRCC from RO by using a fivefold cross-validation strategy. The performance of the classifier was mainly evaluated by areas under the receiver operating characteristic (ROC) curve and accuracy. Results In total, 1029 features were extracted from CMP, NP, and EP. The LASSO regression algorithm was used to screen out the four, four, and six best features, respectively, and eight features were selected when CMP and NP were combined. All five classifiers had good diagnostic performance, with area under the curve (AUC) values greater than 0.850, and support vector machine (SVM) classifier showed a diagnostic accuracy of 0.945 (AUC 0.964 ± 0.054; sensitivity 0.999; specificity 0.800), showing the best performance. Conclusions Accurate preoperative differential diagnosis of chRCC and RO can be facilitated by a combination of CT-enhanced quantitative features and machine learning.

scholarly journals Small world and stability analysis of industrial coupling symbiosis network of ecological industrial park of oil and gas resource cities

2021 ◽  
pp. 014459872098361
Author(s):  
Yanqiu Wang ◽  
Zhengxin Sun ◽  
Pengtai Li ◽  
Zhiwei Zhu
Keyword(s):  
System Analysis ◽  
Oil And Gas ◽  
Small World ◽  
Industrial Park ◽  
Sharp Change ◽  
Connected Subgraph ◽  
Network Efficiency ◽  
Complex Network Theory ◽  
The Stability ◽  
Agglomeration Coefficient

This paper analyzes the small cosmopolitan and stability of the industrial coupling symbiotic network of eco-industrial parks of oil and gas resource-based cities. Taking Daqing A Ecological Industrial Park as an example, we constructed the characteristic index system and calculated the topological parameters such as the agglomeration coefficient and the average shortest path length of the industrial coupling symbiotic network. Based on the complex network theory we analyzed the characteristics of the scaled world, constructed the adjacency matrix of material and information transfers between enterprises, drew the network topology diagram. We simulated the system analysis and analyzed the stability of the industrial coupling symbiotic network of the eco-industrial park using the network efficiency and node load and maximum connected subgraph. The analysis results are as follows: the small world degree δ of Daqing A Eco-industrial Park is 0.891, which indicates that the industrial coupled symbiotic network has strong small world characteristics; the average path is 1.268, and the agglomeration coefficient is 0.631. The probability of edge connection between two nodes in a symbiotic network is 63.1%, which has a relatively high degree of aggregation, indicating that energy and material exchanges are frequent among all enterprises in the network, the degree of network aggregation is high, and the dependence between nodes is high; when the tolerance parameter is 0 to 0.3, the network efficiency and the maximum connected subgraphs show a sharp change trend, indicating that the topology of the industrial coupling symbiotic network of the eco-industrial park changes drastically when the network is subjected to deliberate attacks. It is easy to cause the breakage of material flow and energy flow in the industrial park, which leads to the decline of the stability of the industrial coupling symbiotic network of the eco-industrial park.

CELLULAR NEURAL NETWORKS CAN MIMIC SMALL-WORLD NETWORKS

1999 ◽  
Vol 09 (10) ◽  
pp. 2105-2126 ◽  
Author(s):  
TAO YANG ◽  
LEON O. CHUA
Keyword(s):  
Neural Networks ◽  
Coupling Strength ◽  
Initial Conditions ◽  
Computing Time ◽  
Small World ◽  
Cellular Neural Networks ◽  
Clustering Coefficient ◽  
High Survival ◽  
Game Of Life ◽  
Hole Detection

Small-world phenomenon can occur in coupled dynamical systems which are highly clustered at a local level and yet strongly coupled at the global level. We show that cellular neural networks (CNN's) can exhibit "small-world phenomenon". We generalize the "characteristic path length" from previous works on "small-world phenomenon" into a "characteristic coupling strength" for measuring the average coupling strength of the outputs of CNN's. We also provide a simplified algorithm for calculating the "characteristic coupling strength" with a reasonable amount of computing time. We define a "clustering coefficient" and show how it can be calculated by a horizontal "hole detection" CNN, followed by a vertical "hole detection" CNN. Evolutions of the game-of-life CNN with different initial conditions are used to illustrate the emergence of a "small-world phenomenon". Our results show that the well-known game-of-life CNN is not a small-world network. However, generalized CNN life games whose individuals have strong mobility and high survival rate can exhibit small-world phenomenon in a robust way. Our simulations confirm the conjecture that a population with a strong mobility is more likely to qualify as a small world. CNN games whose individuals have weak mobility can also exhibit a small-world phenomenon under a proper choice of initial conditions. However, the resulting small worlds depend strongly on the initial conditions, and are therefore not robust.

SCALE-FREE AND SMALL-WORLD PROPERTIES OF A SPECIAL HIERARCHICAL NETWORK

Fractals ◽  
2019 ◽  
Vol 27 (02) ◽  
pp. 1950010
Author(s):  
DAOHUA WANG ◽  
YUMEI XUE ◽  
QIAN ZHANG ◽  
MIN NIU
Keyword(s):  
Degree Distribution ◽  
Path Length ◽  
Small World ◽  
Average Path Length ◽  
Clustering Coefficient ◽  
Hierarchical Network ◽  
Scale Free

Many real systems behave similarly with scale-free and small-world structures. In this paper, we generate a special hierarchical network and based on the particular construction of the graph, we aim to present a study on some properties, such as the clustering coefficient, average path length and degree distribution of it, which shows the scale-free and small-world effects of this network.

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